Abstract Machine learning and artificial intelligence (ML/AI) are rapidly becoming an
indispensable part of physics research, with applications ranging from theory and materials …

Scanning probe microscopy (SPM) has revolutionized our ability to explore the nanoscale
world, enabling the imaging, manipulation, and characterization of materials at the atomic …

AFM microscopy from its nature produces outputs with certain distortions, inaccuracies and
errors given by its physical principle. These distortions are more or less well studied and …

Scanning probe microscopy (SPM) is an outstanding nanometrology tool for characterizing
the structural, electrical, thermal, and mechanical properties of materials at the nanoscale …

Many contact models have been proposed for determining the elastic modulus of materials
based on AFM force measurement. However, contact model fitting could be a challenging …

The acceleration of materials discovery has gained paramount importance due to its
potential to overcome constraints in emerging technologies. Extensive exploration has been …

Atomic force microscopy (AFM or SPM) imaging is one of the best matches with machine
learning (ML) analysis among microscopy techniques. The digital format of AFM images …

Convolutional neural nets (CNN) are used to classify and predict bulk mechanical properties
of a series of polymer blends based on their microstructure, as measured by atomic force …